FAI3

Fair AI, descentralization and transparency

Fair AI Network | Descentralization | Transparency | Fair AI Network | Descentralization | Transparency | Fair AI Network | Descentralization | Transparency | Fair AI Network | Descentralization | Transparency |

Fair AI Network | Descentralization | Transparency | Fair AI Network | Descentralization | Transparency | Fair AI Network | Descentralization | Transparency | Fair AI Network | Descentralization | Transparency |

Artificial intelligence requires vast resources:

Data: Large datasets are essential for training and fine-tuning AI models.
Computation: Large computing resources are needed to process complex algorithms and data.
Human Annotation: Expert human input guides the learning process, and ensures the accuracy.



A Threat to Humanity

Bias
We use AI algorithms and may not be aware of bias evaluation.
Centralization
Only few can access computation at that scale.
Version Control
We aren’t aware of changes in versions of code, data, or annotations.


We Propose

Record Predictions

Fairness in AI is critical. We record predictions on the network to evaluate fairness constantly. This allows users to have transparent model evaluation metrics.

On-Chain
Fine-tuning

The network facilitates the fine-tuning of models on-chain. New versions of the model and data are available and constantly evaluated for fairness.

Version Control


Creates a traceable history of how models evolve over time and how different datasets influence their development.

Token Economics

Earn Pay
Usage of your models Deploying a model
Usage of your data Fine-tuning
Helping validate fairness in AI models Making predictions on the network